
After determining the optimal approach, we will now compare our model more thoroughly against the previous SOTA model (CPV-FULL[1]}) in both environments. For craftworld [2]}, we follow the evaluation protocol of [1]}. Both our model and the SOTA model are trained on 50,000 samples from sequences with 1-3 different tasks, and we evaluate each model against sequences with 4,8, and 16 tasks. We also evaluated the model's capability with sequence of tasks with “1,1" being a single task, and “2,2" being a sequence of 2 tasks from 2 reference trajectories. As shown in table REF , our model outperforms the SOTA model in both task success rate as well as performance speed in most cases. In particular our technique leads to a 30% relative increase in the success rate of the most challenging experiment, and a 50% reduction in episode length. This indicates that our model has a better generalization capability when dealing with trajectories with more tasks, as well as when dealing with composed sequence of tasks. In the navigation environment (figure REF ),
the previous SOTA [4]} has a success rate of 94.6%.
while our model is able to achieve a 98.0% success rate. The average efficiency of our agent is also 20%-30% faster than then the previous SOTA.
With the disentanglement of skill and knowledge, we are able to better share useful experiences across different navigation tasks.
